Update app.py

app.py

@@ -254,8 +254,17 @@ def analyze_symptoms(text):
             elif not all("label" in item and "score" in item for item in flattened):
                 logger.warning(f"Missing label or score in flattened result: {flattened}")
             else:
-                prediction = flattened[0]["label"]
-                score = flattened[0]["score"]
+                # Sort by score (descending) and take the highest
+                valid_items = [
+                    item for item in flattened
+                    if isinstance(item, dict) and "label" in item and "score" in item
+                    and isinstance(item["label"], str)
+                    and isinstance(item["score"], (int, float)) and 0 <= item["score"] <= 1
+                ]
+                if valid_items:
+                    sorted_items = sorted(valid_items, key=lambda x: x["score"], reverse=True)
+                    prediction = sorted_items[0]["label"]
+                    score = sorted_items[0]["score"]
         elif isinstance(result, dict):
             logger.debug("Model returned single dictionary")
             if "label" in result and "score" in result:
@@ -394,4 +403,95 @@ async def analyze_voice(audio_file, language="en", user_id="anonymous", consent_
             if prediction == "No health condition detected"
             else f"Possible {prediction.lower()} detected based on symptoms like '{transcription.lower()}', consult a doctor. This is not a medical diagnosis."
         )
-        logger.info(f"Feedback: {fat the BRD requirements, and ensures accurate feedback reflecting the voice input. Let me know how it performs, and share any logs or errors if issues persist!
+        logger.info(f"Feedback: {feedback}, Transcription: {transcription}, Prediction: {prediction}, Score: {score:.4f}")
+        
+        # Save to Salesforce
+        save_to_salesforce(user_id, transcription, prediction, score, feedback, consent_granted)
+        
+        try:
+            os.remove(audio_file)
+            logger.debug(f"Deleted audio file: {audio_file}")
+        except Exception as e:
+            logger.error(f"Failed to delete audio file: {str(e)}")
+        
+        # Speak response
+        await speak_response(feedback)
+        
+        return feedback
+    except Exception as e:
+        logger.error(f"Voice analysis failed: {str(e)}")
+        return f"Error: {str(e)}"
+
+async def test_with_sample_audio(language="en", user_id="anonymous", consent_granted=True):
+    """Test with synthetic audio."""
+    temp_dir = os.path.join(tempfile.gettempdir(), "audio_samples")
+    if not ensure_writable_dir(temp_dir):
+        fallback_dir = os.path.join(os.getcwd(), "temp_audio_samples")
+        if not ensure_writable_dir(fallback_dir):
+            logger.error(f"Temp directories {temp_dir} and {fallback_dir} not writable")
+            return f"Error: Temp directories not writable"
+        temp_dir = fallback_dir
+    
+    sample_audio_path = os.path.join(temp_dir, "dummy_test.wav")
+    logger.info(f"Generating synthetic audio at: {sample_audio_path}")
+    sr = 16000
+    t = np.linspace(0, 2, 2 * sr)
+    freq_mod = 440 + 10 * np.sin(2 * np.pi * 0.5 * t)
+    amplitude_mod = 0.5 + 0.1 * np.sin(2 * np.pi * 0.3 * t)
+    noise = 0.01 * np.random.normal(0, 1, len(t))
+    dummy_audio = amplitude_mod * np.sin(2 * np.pi * freq_mod * t) + noise
+    try:
+        soundfile.write(dummy_audio, sr, sample_audio_path)
+        logger.info(f"Generated synthetic audio: {sample_audio_path}")
+    except Exception as e:
+        logger.error(f"Failed to write synthetic audio: {str(e)}")
+        return f"Error: Failed to generate synthetic audio: {str(e)}"
+    
+    if not os.path.exists(sample_audio_path):
+        logger.error(f"Synthetic audio not created: {sample_audio_path}")
+        return f"Error: Synthetic audio not created: {sample_audio_path}"
+    
+    mock_transcription = "I have a cough and sore throat"
+    logger.info(f"Mock transcription: {mock_transcription}")
+    prediction, score = analyze_symptoms(mock_transcription)
+    feedback = (
+        "No health condition detected, consult a doctor if symptoms persist. This is not a medical diagnosis."
+        if prediction == "No health condition detected"
+        else f"Possible {prediction.lower()} detected based on symptoms like '{mock_transcription.lower()}', consult a doctor. This is not a medical diagnosis."
+    )
+    logger.info(f"Test feedback: {feedback}, Prediction: {prediction}, Score: {score:.4f}")
+    
+    # Save to Salesforce
+    save_to_salesforce(user_id, mock_transcription, prediction, score, feedback, consent_granted)
+    
+    try:
+        os.remove(sample_audio_path)
+        logger.debug(f"Deleted test audio: {sample_audio_path}")
+    except Exception:
+        pass
+    return feedback
+
+async def voicebot_interface(audio_file, language="en", user_id="anonymous", consent_granted=True):
+    """Gradio interface wrapper."""
+    return await analyze_voice(audio_file, language, user_id, consent_granted)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=voicebot_interface,
+    inputs=[
+        gr.Audio(type="filepath", label="Record or Upload Voice (WAV, MP3, FLAC, 1+ sec)"),
+        gr.Dropdown(["en", "es", "hi", "zh"], label="Language", value="en"),
+        gr.Textbox(label="User ID (optional)", value="anonymous"),
+        gr.Checkbox(label="Consent to store data", value=True)
+    ],
+    outputs=gr.Textbox(label="Health Assessment Feedback"),
+    title="Smart Voicebot for Public Health",
+    description="Record or upload a voice sample describing symptoms (e.g., 'I have a cough') for preliminary health assessment. Supports English, Spanish, Hindi, Mandarin. Not a diagnostic tool. Data is encrypted and stored with consent. Complies with HIPAA/GDPR."
+)
+
+if __name__ == "__main__":
+    logger.info("Starting Voice Health Analyzer")
+    # Test with synthetic audio
+    loop = asyncio.get_event_loop()
+    print(loop.run_until_complete(test_with_sample_audio()))
+    iface.launch(server_name="0.0.0.0", server_port=7860)